The present work investigates instantaneous synchronization in multivariate signals. It introduces a new method to detect subsets of synchronized time series that do not consider any baseline information. The method is based on a Bayesian Gaussian mixture model applied at each location of a time-frequency map. The work assesses the relevance of detected subsets by a stability measure. The application to Local Field Potentials measured during a visuo-motor experiment in monkeys reveals a subset of synchronized time series measured in the visual cortex.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jphysparis.2011.07.018 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Bayesian Analysis of Censored Linear Mixed-Effects Models for Heavy-Tailed Irregularly Observed Repeated Measures.
Stat Med
February 2025
Department of Statistics, University of Connecticut, Storrs, Connecticut.
The use of mixed-effect models to understand the evolution of the human immunodeficiency virus (HIV) and the progression of acquired immune deficiency syndrome (AIDS) has been the cornerstone of longitudinal data analysis in recent years. However, data from HIV/AIDS clinical trials have several complexities. Some of the most common recurrences are related to the situation where the HIV viral load can be undetectable, and the measures of the patient can be registered irregularly due to some problems in the data collection.
View Article and Find Full Text PDFBayesian design of clinical trials with multiple time-to-event outcomes subject to functional cure.
J Biopharm Stat
January 2025
Department of Biostatistics, University of North Carolina, Chapel Hill, USA.
With the continuous advancement of medical treatments, there is an increasing demand for clinical trial designs and analyses using cure rate models to accommodate a plateau in the survival curve. This is especially pertinent in oncology, where high proportions of patients, such as those with melanoma, lung cancer, and endometrial cancer, exhibit usual life spans post-cancer detection. A Bayesian clinical trial design methodology for multivariate time-to-event outcomes with cured fractions is developed.
View Article and Find Full Text PDFRepresentational geometry explains puzzling error distributions in behavioral tasks.
Proc Natl Acad Sci U S A
January 2025
Department of Economics, Columbia University, New York, NY 10027.
Measuring and interpreting errors in behavioral tasks is critical for understanding cognition. Conventional wisdom assumes that encoding/decoding errors for continuous variables in behavioral tasks should naturally have Gaussian distributions, so that deviations from normality in the empirical data indicate the presence of more complex sources of noise. This line of reasoning has been central for prior research on working memory.
View Article and Find Full Text PDFAdvanced Monte Carlo for Acquisition Sampling in Bayesian Optimization.
Entropy (Basel)
January 2025
Instituto Universitario de Investigacion en Ingenieria de Aragon (I3A), Universidad de Zaragoza, 50018 Zaragoza, Spain.
Optimizing complex systems usually involves costly and time-consuming experiments, where selecting the experiments to perform is fundamental. Bayesian optimization (BO) has proved to be a suitable optimization method in these situations thanks to its sample efficiency and principled way of learning from previous data, but it typically requires that experiments are sequentially performed. Fully distributed BO addresses the need for efficient parallel and asynchronous active search, especially where traditional centralized BO faces limitations concerning privacy in federated learning and resource utilization in high-performance computing settings.
View Article and Find Full Text PDFGraphical Model Selection to Infer the Partial Correlation Network of Allelic Effects in Genomic Prediction With an Application in Dairy Cattle.
J Anim Breed Genet
January 2025
Departamento de Ciencias Agrícolas y Pecuarias, Universidad Francisco de Paula Santander, Cúcuta, Colombia.
We addressed genomic prediction accounting for partial correlation of marker effects, which entails the estimation of the partial correlation network/graph (PCN) and the precision matrix of an unobservable m-dimensional random variable. To this end, we developed a set of statistical models and methods by extending the canonical model selection problem in Gaussian concentration, and directed acyclic graph models. Our frequentist formulations combined existing methods with the EM algorithm and were termed Glasso-EM, Concord-EM and CSCS-EM, whereas our Bayesian formulations corresponded to hierarchical models termed Bayes G-Sel and Bayes DAG-Sel.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!